Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.

Flood disaster mostly happens due to instant heavy rain fall or sudden increase in water level in rivers. Such natural disaster may results in excessive loss of human life and property. However, it is very important to aware the residential areas before and during the disaster by disseminating information. With the rapid development of devices embedded with Internet-of- Things (IoT), this may bring a lot of benefits to propagate the information among people. In this work, we proposed a decentralized disaster detection approach using image data. The proposed framework consist of set of sensor devices capable of capturing the images. Each device is able to process the image and generate warning alarm based on its decision. For detection of flood, we applied thresholding-based segmentation and morphological operations. We performed extensive experiments to validate our proposed approach. For analysis purpose, we considered images taken from different distance and our proposed approach provides promising results.

Mobile devices generate massive amounts of data that is used to get an insight into the user behavior by enterprise systems. Data privacy is a concern in such systems as users have little control over the data that is generated by them. Blockchain systems offer ways to ensure privacy and security of the user data with the implementation of an access control mechanism. In this demonstration, we present ChainMOB, a mobility analytics application that is built on top of blockchain and addresses the fundamental privacy and security concerns in enterprise systems. Further, the extent of data sharing along with the intended audience is also controlled by the user. Another exciting feature is that user is part of the business model and is incentivized for sharing the personal mobility data. The system also supports queries that can be used in a variety of application domains.

In existing PMIP-SAE based mobile networks, all the communication traffic go through the centralized Local Mobility Anchor (LMA). Hence the central node, LMA, suffers from huge packet processing overhead and the traffic is delivered via non-optimal routes. In order to overcome these drawbacks, we apply the localized routing to mobile networks based on PMIP-SAE mobility management scheme, while introducing additional control messages of Localized Routing Initiation (LRI) and Local Routing Acknowledgment (LRA). Numerical analysis shows that our proposed scheme performs better than the existing scheme in packet delivery cost, tunneling overhead, and throughput.